Environmental-economic optimization for implementation of parabolic collectors in the industrial process heat generation: Case study of Mexico
Graphical abstract
Introduction
The industrial sector represents the second greatest energy consumer in Mexico, with 34% of the total national demand (SENER, 2018a). Of this amount, two-thirds are required in the form of heat, with temperature ranges between 50 °C and 200 °C, for the development of industrial processes such as drying, cooking, cleaning, blanching, heating, dehydration, pasteurization, and among others. In the Mexican scenario, more than 95% of the heat produced for industrial applications is met with conventional fossil fuel-based heating systems, implying a degradation of the local environment and consequently leaving its footprints over the entire country. According to the National Inventory of Gases Emissions and Greenhouse Effect Compounds, fossil fuel emissions produced by industrial activities are equivalent to 17% of the total greenhouse gases (GHG) generated in the country (73.9 million tons of CO2) (INECC, 2018). This amount is estimated to increase in the coming years, based on the continuous growth of the national industrial energy demand (27.82 PJ per year reported in the last 10 years) (SENER, 2018b, 2018c; 2018d). Moreover, the wide dependence on fossil fuels drastically impacts on the economic production due to the hydrocarbon deposits reduction, the volatility of the markets, and the constant increase in the prices of petroleum and its derivatives. This makes necessary to devise measures so that the country’s industrial growth that conforms to the new guidelines set by international organizations. The objective is to minimize GHG emissions and ensure the adequate industrial sector heat, allowing its continued operation and development.
In recent years, solar heat generation systems have emerged as a CO2 emissions reduction alternative to satisfy a substantial amount of heat industrial demand and diminish the cost associated with fuel consumption (Farjana et al., 2018). These systems can be coupled to conventional heat sources for industrial water heating and steam generation, reaching temperatures up to 280 °C. Studies at different industry branches have proven the ecological benefits of using solar heating industrial process (SHIP) to reduce the anthropogenic impact over global warming. Sharma et al. (2017a) and Schnitzer et al. (2007) estimated the potential of SHIP systems and corresponding mitigation of GHG emissions in the dairy industry. Ramos et al. (2013) analyzed the reduction of fossil fuel consumption and GHG emissions by incorporating SHIP systems in the Mexican textile and food industry. Carbon mitigation potential in the paper industry by SHIP systems was investigated by Sharma et al. (2016). Similarly, GHG mitigation potential of SHIP systems in producing cotton based textiles in India was studied (Sharma et al., 2017b). Additionally, research studies conducted in several countries such as Cyprus (Kalogirou, 2002), India (Kar et al., 2016), Germany (Lauterbach et al., 2012), Australia (Fuller, 2011), and Argentina (Lillo et al., 2017), have demonstrated the benefits of integrating solar heat into industrial activities.
Mexico has favorable conditions for the harnessing of the solar resource with radiation levels ranging from 4.2 kWh·m−2 to more than 6.0 kWh·m−2 per day (Pérez-Denicia et al., 2017). Mexican solar industrial heat generation is currently one of the largest developments around the world with a production capacity of 0.617 GJ in a net extension of 23,049.0 m2 (Solar Payback, 2018). From this installed capacity, 84% of the SHIP facilities have been carried out in the food and beverage sector, applied mainly for heating and pasteurization processes. Among the various solar thermal technologies, small-scale parabolic trough collectors (PTC) have become one of the most attractive, exhibiting a notable rise in the national solar heat market (Ortega, 2018). Small-scale PTC technology covers 34% of the national installed thermal capacity and represents 50% of the SHIP projects in the country, presenting a hegemony mainly in the dairy production and animal feed manufacturing (Table 1). Nevertheless, the use of solar resources in the country is yet limited, representing only 0.04% of the total energy consumed in the industrial sector. Estimations of the International Renewable Energy Agency (IRENA, 2015) indicate that Mexican territory can increase its solar thermal capacity up to 41 PJ by 2030. Unfortunately, the national trends in the use of SHIP systems have an increase rate of 13% per year, yielding a final solar thermal energy production of 2.9 PJ. The main drawbacks for the penetration of this technology at the industry are the high cost of initial investment and the subsidies to fossil fuels costs (Solar Payback, 2018). Additionally, the performance of SHIP systems is affected by other factors such as climatic conditions, weather fluctuations over the day, required thermal loads and the facility design; converting them into highly nonlinear complex processes (Thaker et al., 2017). This problem has a more notable impact in countries with a vast territorial extension such as Mexico, due to the wide variety of existing climates.
In this context, few studies have been directed to the assessment of SHIP systems financial viability, reporting their results by different economic indicators. Sharma et al. (2018) evaluated the SHIP systems economic viability for Indian’s dairy industry considering the payback period and the internal rate of return. May Tzuc et al. (2018) estimated the investment viability of solar thermal plants for the region of Jalisco, Mexico, employing the Net Present Value (NPV). Kumar et al. (2019) and Jaramillo et al. (2013), presented studies where the cost of level heat production was used to define the economic competitiveness of SHIP systems. Allouhi et al. (2017) presented the use of the Total Life Cycle Cost (TLCC) to improve the profitability of a SHIP system for a case of the dairy industry in Morocco. However, to the best knowledge of the authors, there are not studies focused on the balance between the financial profitability and the ecological impact of the SHIP systems, notwithstanding it is a fundamental element on the fulfillment of the environmental perspectives for several countries. Therefore, there is a need to find suitable design configurations of SHIP systems that guarantee both the optimum compliance with environmental legislation and the profitability of plant implementation.
Based on the aforementioned, it is quite challenging to achieve a fair equilibrium in an environmental-economic analysis because it is a non-linear combination of certain tuning parameters. Thereby, different research studies (Cui et al., 2017) have concluded that such a problem can be well-posed only through a simultaneous optimization analysis. Currently, Multi-objective optimization (MOO) algorithms based on artificial intelligence (AI) have been proposed as a solution in the designing and sizing of several thermal systems to get the trade-off relation between economic (or energetic) and environmental factors. In the work of Abido (2003), a MOO evolutionary algorithm was used to meet the load demand of a thermo-electric power plant considering the fuel operating cost and the atmospheric emissions as objective functions. The study identified the generators power operating conditions to reach the balance between reduction of fuel consumption and the minimization of pollution emissions. In the study of Starke et al. (2018), a methodology for design and sizing a hybrid solar/photovoltaic (CSP/PV) concentration power plant by MOO was presented. A couple of metamodels to compute the levelized cost of energy (LCOE) and the capacity factor were performed using a linear interpolation method and a database generated by the TRNSYS software. According to the results, the entailing of the metamodel and evolutionary optimization algorithms were able to estimate lower LCOE than conventional CSP systems as well as capacity factors above 85%. Torres-Rivas et al. (2018) presented a MOO approach to optimize the thickness of several bio-based building insulation materials at different climate conditions considering the cost and environmental impact as objective functions. Schröders and Allelein (2018) analyzed the economic competitiveness of a solar tower to replace natural gas on an ammonia production process for an electric generation plant. By using Mixed Integer Linear Programming (MILP) was proposed in a simultaneous optimization of NPV, ammonium generation, electrical purchase, sales amount, and sizing and operation mode of the facility. The results revealed that energy supply by the solar tower does not compete with the heat supply of a heater fired by natural gas, being necessary to raise the natural gas price (2.7–8.1 €·MW−1) to make the system competitive. Yi et al. (2018) conducted a thermo-economic-environmental optimization for the design of a waste heat driven Organic Rankine Cycle, considering the net power output and the environmental impact as the objective functions. The results suggest that environmental impact can be substantially reduced if decision-makers are willing to compromise the economic benefit. Breen et al. (2019) developed a multi-objective optimization method for a dairy process by using the net profit and CO2 as objective functions. In the study of Luger and Rieberer (2018), they demonstrated the use of MOO with a metamodel to optimize the design of an R744 (CO2) refrigerant cycle using the electric power demand, equipment cost, installation volume, equipment mass, and noise emissions as objective functions. Pareto’s results showed a wide range of possible optimal solutions, which depend on the final purpose of the HVAC system (such as increasing the cost by reducing power demand) given a decision-maker.
As is described above, MOO methods have proven to be powerful tools for environmental-economic analysis as well as for the optimization in sizing and design of energy systems due to their robustness and adaptability in complex nonlinear and intermittent processes. In this sense, the optimal design of solar thermal systems for industrial heat generation represents an important contribution to direct the growth of this sector towards a clean thermal energy production into countries with ample solar resource such as Mexico. Besides, given the current trends in terms of environmental impact reduction, this area is a niche of relevant research in the coming years.
Therefore, this work presents an optimization framework to improve the design and operability of SHIP systems based on the trade-off between investment viability and environmental benefits, as well as the guidelines for selecting the appropriate configurations through a decision-making scheme. The work focused on the conditions of the Mexican industrial sector taking as a case study a hybrid solar system, composed of small-scale PTC, coupled to a pre-existing food industrial process. The study considers four of the most common Mexican climatic regions and the most representative heating fossil fuels in the national industrial sector. A MOO was applied to identify the most suitable SHIP system design conditions considering as objective functions the amount of CO2 mitigated (ACM), the total life-cycle cost (TLCC), and the net present value (NPV). Afterward, a decision-making method is used for selecting the appropriate configurations according to the environmental, economic, and clean thermal energy requirements. The main goals of this study can be synthesized as follow:
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Develop a novel computational methodology based on numerical experiments, surrogated AI models, and multi-objective optimization to increase the environmental performance and profitability of the SHIP systems from improvements in their design configurations.
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Conduct a comprehensive decision-making process for selecting the best system configuration and increase its entry into the industrial heat market, based on an environmental-economic analysis and preference of the investors.
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Evaluate environmental and thermo-energetic performance, as well as profitability of optimized SHIP systems under various climatic regions and heating fossil fuel scenarios commonly used in the Mexican industrial sector.
The proposed methodology aims to facilitate the analysis and integration of solar heat generation technology within industrial processes, especially for countries with a coarse solar resource, through a perspective that encourages investment in this technology while avoiding to the extent possible losses in the mitigation of pollutants.
Section snippets
Industrial process
Food processes are the main niche of opportunity for the implementation of solar technology in the Mexican industrial sector. Dairy processing represents a particular case as it is the third most important branch of the industry in Mexico, with a quasi-constant annual production, and it is one of the activities that is carried out throughout the entire national territory. Therefore, the industrial process considered as a case study corresponds to the dairy industry, which uses heat energy in
Computational methodology
Fig. 3 illustrates the methodology developed for this study, consisting of four stages. In the first stage, a PTC-SHIP system transient model is employed to carry out numerical experiments evaluating various solar plant designs and create a performance database. In this stage, the indicators used to define the environmental and economic aspects of each scenario are ACM, TLCC, and NPV. In the second stage, a surrogate AI model of the PTC-SHIP system is developed using the results of the
Surrogate ANN model
Various ANN models, with different numbers of neurons in the hidden layer, were trained and statistically evaluated (Eq.(10), (11), (12))) to determine their estimation accuracy by a trial-and-error approach. The best surrogate model to simultaneously predict the ACM, TLCC, and NPV of the proposed PTC-SHIP system was given by 100 neurons at the hidden layer (Fig. 4). The substantial number of hidden neurons demonstrates the high complexity and nonlinearity between the process under study and
Conclusion
This paper presented a novel computational framework based on an environmental-economic perspective to improve the sizing of PTC-SHIP systems and facilitate their incorporation into industrial heat generation market. Results demonstrate that use of artificial neural networks allows generating a multivariable mathematical model which synthesizes the operability experience of the PTC-SHIP system based on the history of operational data, costs and environmental contributions, globalizing the
Acknowledgment
The first author thanks CONACYT for the financial support granted for the development of this work through the scholarship number No. 44102 (CVU 627799). All authors are thankful for the support of editorial board and anonymous reviewers for helping us in the quality improvement of this document.
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2022, Sustainable Energy Technologies and AssessmentsCitation Excerpt :This proposed design was based on the works reported by [8,31,32] who presented similar studies of parabolic trough solar collectors integrated into industrial processes. Finally, for this research work, 134 PTSC Power Trough type 110 are considered which represent an average value of the optimized results obtained by [8]. This value was proposed to preserve the parameters and dimensions of the system homogeneously to make the appropriate comparisons between the different climates.